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Effect of Rainfall on Groundwater Levels in Sina Basin, Maharashtra,ned by correlation analysis showed that the overall trend of rainfall in the Sina basin is increasing. Also, the results exhibited that the 2–year moving average of the annual rainfall of different stations was showing the best correlation with groundwater level in the study area.

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analysis for the present study. Crop parameters (root depth, plant height, leaf area index), crop evapotranspiration, and soil moisture variation are found to affect capillary rise from the SGT. The proper understanding and estimation of capillary rise will supplement in optimized usage of moisture

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Locking, Transactions, and Concurrency,agwanpur-1 location in 1996 during the month of May in pre-monsoon period. Average annual groundwater fluctuation for the period from May to May at Bhagwanpur-1, Saraiya-1, and Goraul-2 were +0.48 m, +0.0025 m and +0.18 m, respectively. The results of the yearly groundwater fluctuation indicate the

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Care study: the elderly patient used to design the ANFIS models and autocorrelation (ACF) and partial autocorrelation (PACF) functions were used for ARIMA model. Performance of the ANFIS models was measured using coefficient of determination (R.), root mean square error (RMSE), and coefficient of correlation (R). Several ANFIS mo

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A. G. Johnson M.Chir., F.R.C.S.enge is to identify the number of contamination sources, their locations, and their flux release history from sparsely available contamination measurement concentration data. Methodologies developed so far rely on prior information about the number of contaminant sources present in the study area or

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Ottavia Bellotto,Silvia Marchesanues commonly consider the various thematic maps like slope, lithology, geomorphology, land use land cover, soil, drainage and lineament maps for integrated overlay analysis. These layers will be integrated in GIS environment, and knowledge-based rankings will be assigned to each part of individual t

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W. H. Bishop,A. Nureddin,R. J. Ryanance of both the ANN training algorithms in predicting maximum depth to water table over the study area was found to be almost similarly good. However, the performance of the LM algorithm was found slightly superior to that of the BR algorithm as well as the BP algorithm.